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Record W2148060363 · doi:10.5539/mer.v4n1p27

Determination of Preload of Double-Row Tapered Roller Bearing Used for Supporting Direct-Drive Wind Turbine Rotor

2013· article· en· W2148060363 on OpenAlexvenueno aff
Yunfeng Li

Bibliographic record

VenueMechanical Engineering Research · 2013
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
FundersEducation Department of Henan Province
KeywordsPreloadBearing (navigation)Structural engineeringRotor (electric)StaticsTurbineMoment (physics)Load distributionLoad bearingEngineeringMechanical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Appropriate axial preload is necessary for the double-row tapered roller bearing used for supporting the rotor of the direct-drive wind turbine; its function is to ensure the rolling motions of the rollers and the long fatigue life of the bearing as far as possible. For this purpose, statics model of the preloaded bearing under the combined action of radial load, axial load and tilting moment load was established firstly; then, for a set of selected preload values which are different proportions of the dynamic equivalent axial load of the external bearing loads, the corresponding loaded roller number, maximum roller load and bearing fatigue life were obtained; thirdly, the effects of different preloads on the calculated indicator values were analyzed, result show that preload can improve the uneven load distribution among the rollers, the preload value also influence the rolling roller number and bearing fatigue life. A preload of 0.5 times of the dynamic equivalent axial load was selected as a trade-off between the rolling roller number and bearing fatigue life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.294
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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